The Data Fabric Evolution
Modern businesses rely heavily on data-driven insights to measure organizational performance, make informed decisions and identify areas of improvement. Businesses' primary services and products depend on data to help generate revenue, but harnessing it to its fullest potential requires the right solutions.
Data management evolution has made it easier for enterprises to increase data pipelines and security to streamline the data integration and transformation process. One of the most important data-centric architectures that has emerged is data fabric, which allows businesses to take control over their data and speed up their operations.
What Is Data Fabric?
The definition of data fabric is an integrated virtual layer of data within a network-based design — similar to how the human brain works with connecting processes. Data fabric emphasizes singularity in the management, distribution and securing of data. It connects all of an organization's data, processes and software to improve standard integration and reduces build times.
Data fabric leverages continuous analytics between human and machine capabilities to support the design and use of reusable data connected from different applications. This process helps discover new, relevant relationships between data points and provides insight to help with decision-making processes. Data fabric provides more value and meaningful data ownership through more rapid, universal access than traditional data management procedures.
For example, data fabric can be useful for a supply chain leader as they add new data assets to existing relationships between production and supplier delays to make changes that improve the entire process. Data fabric exists to provide an enhanced way to manage enterprise data and replace copies with controlled access — giving the control back to data owners to share with collaborators.
Benefits of Data Fabric Architecture
The concept of data fabric is a revolutionary accomplishment for managing and securing data. With the internet and cloud platform technology, it becomes more and more difficult to own data when there are thousands or millions of copies of it.
Data fabric architecture helps control data ownership and maximizes the value of information being spread ineffectively. This process also helps organizations align their resources to offer secure and streamlined access to data within a complex distributed network environment. Overall, the top benefits of data fabric architecture include:
- Improved adaptability: Data fabric offers simple configuration, allowing organizations to easily redesign or modify the architecture to fit changing needs.
- Trusted data: Eliminating data silos — which limit an organization's ability to use and manage data analytics — allows data to spread across the organization and reduce inconsistencies.
- Increased reusability: Organizations only have to build data fabric once. Then, they can disperse it globally without adjusting or adapting to new data models.
- Faster turnaround time: Data fabric offers integration in real time through artificial intelligence (AI) auto-discovery and virtualization, speeding up time to market instead of taking days and months to complete.
- Streamlined process and reduced costs: Implementing more consistent controls across all data minimizes much of the infrastructure complexity and operations effort.
- Enterprise-wide data security: Data fabric architecture helps with security and governance issues regarding compliance requirements, privacy regulations and security breach incidents.
With these benefits, organizations can access data and analytics to experiment and drive value through enhanced management, governance and security.
Data Fabric Architecture Capabilities and Uses
Once an organization understands its regulatory and compliance needs, along with its data consumption, it can fully use and benefit from its data fabric. It's important to understand all of these areas to sort out any existing issues and focus on discovering new capabilities. Here are some of the top data fabric uses and examples of data fabrication.
A data fabric connects data and applications between IT and physical systems, enhancing overall security within the organization. Organizations can tie together key reader information to open doors to correlate with event data on computer systems within the facility. This improvement would allow teams to perform a better analysis of behavior to create and deploy instant security alerts when needed.
It's crucial for businesses to understand changes that occur over time across all departments, and a data fabric is critical to this understanding. A data fabric acts as a map of vital information and removes all unnecessary details to help visualize business outcomes.
Dealing with machine learning and AI can be complicated, and a data fabric allows organizations to test and train their abilities to structure their business processes across multiple applications.
The Big Picture
Organizations can use data fabric to create a holistic customer view and view of their business across all departments and activities. As the data fabric architecture connects data from various roles and customer activities, enterprises get real-time data of potential revenue realization, customer onboarding time and sales activities. This process helps organizations get a better grasp on customer satisfaction.
An organization can capture information about how a customer uses its services and tie in other customer support requests to coordinate better sales activities. Data fabric architecture correlates these different data sources to provide valuable recommendations and improved analytics that organizations can use to enhance their performance.
A data fabric architecture can also create a more accessible data marketplace to make it simpler to connect different elements and data sources into new versions and models. With this new marketplace, data engineers can develop an infrastructure they can use in many different ways instead of generating new infrastructure for each use case.
The data marketplace will also help manage an organization's data needs for both current and future requirements.
AI Data Collaboration
AI data collaboration is critical for helping businesses make better-informed decisions, but it requires extensive access to integrative data.
A data fabric can support the delivery of high-integrity data to AI applications quickly and efficiently while detecting fraud and finding new ways to improve predictive analytics. A data fabric also supplies streamlined access to real-time data to help with predictive maintenance.
Predictions and Actions
Data fabrics can transform how organizations evolve over time by simplifying triggered actions and predictions. Organizations can use data fabrics to train, configure and deploy these prediction algorithms and actions that spread across application endpoints.
These use cases cover many different aspects of an organization, including customer retention, marketing, ad optimization and orchestrated selling.
Data Fabric Challenges and Obstacles
While data fabric provides extensive benefits, there are always elements to consider when implementing new processes into your organization. Your team may envision a perfect data fabric, but it's important to also think about practicality.
With data fabric solutions, there is a wide variety of storage locations, data management policies and databases, and it can take a while for the fabric to coordinate these differences. Otherwise, data silos will continue to harbor most of the information and cause more limitations on the information available in the data fabric.
To deal with this obstacle, organizations will have to develop a foundation for the data fabric using a unified platform. Using multiple platforms can negatively impact operational efficiency, so this new technology needs to be extendable to the entire company. This unification is important — but it also has its risks when done through virtualization. Using various locations can make it difficult for applications to access the exact location of information in a data fabric.
Another challenge can be the difference in APIs and query languages and different access mechanisms found in various databases. A robust data fabric strategy should support a common query and access mechanism, but it should not restrict specialized query languages and APIs. Otherwise, current applications may not run properly. This challenge directs back to the goal of creating a uniform and harmonized fabric access and query technology as organizations modify and adjust their applications.
Evolving Data Platforms
The evolution of data platforms over the last decade came from the need to integrate and centralize data from multiple, separate systems. The first glimpse of data platforms, called data warehouses, helped make integrated transactional data available for analytic purposes. Data warehouse contained structured, filtered data, which analysts processed for a specific use.
When data warehouses could no longer handle the storage and management needs of enterprise data, second-generation data platforms arrived, called data lakes. These data lakes contained large amounts of raw data without a defined purpose. Eventually, data warehouses and data lakes were not enough to solve the gap between the data's origin point and where it transformed into insights through analytics.
As the analytic and transactional purposes for this enterprise data evolved with more distinctions, it created many issues, such as:
- Time lags: As data travels between these systems, it has to be integrated and transformed several times, which often creates a time lag between the creation of the data and when it becomes useable for analytics. This means that the analytical systems, which often rely on human interpretation and insights, could potentially be looking at outdated and unreliable information for decision-making.
- More specialized skills: Gaining insights and data through algorithms and data architectures requires expertise in various fields that many business users don't have, which can create a gap between business and technology departments within an enterprise.
Today, there are platforms available that can tackle these issues and transform raw data into information and insights, like Dextrus. Dextrus is a cloud data platform that helps organizations with:
- Self-service data ingestion
- Machine learning modeling
With these features, businesses can create real-time streaming data pipelines in minutes and easily maintain their cloud data lake for reporting and advanced analytics needs. Using visualizations and dashboards, Dextrus also helps organizations gain insights and operationalize data fabric for machine learning models for classifications and predictions.
Redefining How Data Is Managed
Data fabric is the fourth generation of data platform architecture. Data fabric makes data easily accessible whenever and wherever it is needed and minimizes or eliminates complexities in the data transformation and integration process.
Data fabrics are a newer, efficient way to store data because they consist of multiple data warehouses, data lakes and other technology that make it easier to support the holistic data lifecycle. While one data fabric node provides raw data, another can perform analytics in record time so the decision-making process is more straightforward.
Data fabric offers a new opportunity to transform operating models and enterprise cultures and promotes unified governance, which helps makes data more reliable. As businesses use the data to innovate, they will also find it easier to collaborate and create a common purpose while managing shared data assets. Though data fabrics are still evolving as an emerging technology, they continue to provide faster data-driven insights than before.
What Data Analysts Need to Know About Data Fabric Architecture and Enterprise Businesses
To effectively implement and manage a data fabric architecture, it's important for businesses to know the essential elements and capabilities of data fabric design and ensure a strong technology base. In addition, companies must also evaluate their existing data management processes and tools to identify areas of improvement. Here are the top four key components of a comprehensive data fabric.
- Collecting and analyzing numerous forms of metadata: Within the data fabric, there must be a system or process that allows the fabric to identify, connect and analyze different types of metadata between business, operational, social and technical departments to collect key contextual information.
- Converting passive metadata to active metadata: Businesses must activate metadata through data fabric that can analyze information for key statistics and metrics, graphically depict metadata to show business relationships and leverage that data to enable algorithms and advanced predictions.
- Creating and optimizing knowledge graphs: Using integration tools and standards, data analysts can develop knowledge graphs, which make it easier to analyze and interpret data through semantics, making it more valuable for operational use cases.
- Having a solid and diverse data integration backbone: Data fabric should support both IT and business users and remain compatible with many different types of data delivery, such as data virtualization, streaming, messaging and replication.
3 Ways Data Fabric Can Support Your Business
With the benefits, use cases and limitations of data fabric in mind, here are the three primary ways it can improve and support your business needs.
1. Speedy Tech
The digital transformation of data fabric helps accelerate business performance modification within an organization. While AI projects can take significant time to establish and connect with different sources within the company, data fabric speeds up the time it takes to provide access to data for authorized business units and people.
Data fabric also places AI and data in the hands of business leaders by supporting automatic machine learning — allowing businesses to apply data easily.
Data analysts spend a large portion of their time searching for data sets, but data fabric removes this obstacle and makes it easier and faster to find the answer to a question.
2. Scaling Innovation
Many companies deploy and manage data in several countries or regions at a time. To effectively and efficiently scale new IT and AI across these global locations can be very time-consuming and complicated. Data fabrics' semantic layer can help organizations skip many of these tedious steps and let companies scale their data quickly while keeping their operations innovative and productive.
3. Trusted Data
Data fabrics help employees understand, interpret and use their data and analytics to create value and better serve their customers and shareholders. This process makes the data accessible to everyone and provides trusted information across the organization. For example, if an authorized employee in the marketing department needs to view data from procurement or sales, they can easily access that data just as seamlessly as marketing data.
Transform Your Business Today With RightData
Many businesses require high-level data maintenance and security to stay on top of insights and remain productive. At RightData, we can help you cover the entire data testing and building process and empower you with the right tools to accelerate data innovation.
Our goal is to provide you with the capabilities and solutions you need throughout your data analytics journey to arrive at the best decisions and see the most value. We care about our client's success and want to collaborate with you to help you achieve your goals and objectives based on reliable, quality data with our RDt and Dextrus products for your data pipeline. Contact us today to learn more about our solutions or request a free demo online.